In this series, I took a close look at these 5 areas of the data science or machine learning development workflow, telling you what the cloud approach looks like versus the local approach, explaining the pros and cons of each, and describing a few tools you might want to try.
Keywords: python, machine learning, cloud computing
Data science model deployment can sound intimidating if you have never had a chance to try it in a safe space. In this blog post, I give the end-to-end explanation of how you go from zero to a deployment that you can share with others.
Keywords: python, HTML, machine learning, deployment, API
List comprehensions are incredibly powerful, but also can be unintuitive and confusing. I wrote a guide to understanding and using them, designed for those who use for-loops in their regular programming.
Keywords: python, programming, beginners
Generative Adversarial Networks (GANs) are an increasingly popular and very powerful form of computer vision deep learning, and they require a whole lot of compute to be done effectively and speedily. This is just the sort of use case where parallelization with Dask clusters can make a difference in your workflow, so I wrote a tutorial on how to do it!
Keywords: python, machine learning, deep learning, dask
I wrote a blogpost to accompany several talks and a github repo all about using different Python visualization tools. Get the code at https://github.com/skirmer/new-py-dataviz.
Keywords: python, data visualization
Do you love pandas, but hate when you reach the limits of your memory or compute resources? Dask gives you the chance to use the pandas API with distributed data and computing. In this article, you’ll learn how it really works, how to use it yourself, and when/if to switch.
Keywords: python, pandas, dask
Lazy evaluation is the core of parallelization, but it doesn’t have to be confusing or complicated — in this guide, learn the basic concepts you need to get started! I use dask to demonstrate but this is useful for anyone trying to get the hang of parallel computation. Keywords: python, parallelization, dask
You should not require or ask for a generic STEM Ph.D. for data scientist candidates. In this blog post, I give a detailed argument on this and discuss the critiques of the practice.
Keywords: data science, social science
I use the Stanford Dogs dataset again, this time to demonstrate accelerating transfer learning to improve Resnet50. The inner workings of how PyTorch supports multi-machine, multi-GPU training can be confusing but I have deciphered it for you here.
Keywords: machine learning, python, deep learning
I use the Stanford Dogs dataset to demonstrate accelerating an image classification problem with GPU Clusters. If you have been thinking about GPUs but don’t know where to start, or what they might be good for, I recommend this as a place to start!
Keywords: machine learning, python, deep learning
Job scheduling is what takes academic machine learning to production level for real business or project value. I went through three tools (cron, Airflow, and Prefect) in this article and discussed the pros and cons to each. Depending on your task and circumstances, any one of these tools might be what you need.
Keywords: python, job scheduling, airflow
In concert with my presentation at ODSC Europe 2020 I wrote a blog post to discuss why and how you might use scheduled jobs to make your data infrastructure work better for modeling and machine learning.
Keywords: job scheduling, airflow, data warehousing
I appeared in a video series for Uptake about how modeling industrial failures work - this was really fun, and I hope people will give it a look! I talk about a specific failure of diesel locomotive hardware, and how I solved it while I was working at Uptake.
I wrote a silly R package called radlibs that allows you to make your own madlibs. Then I wrote a version in Python. Then I added them to CRAN and pypi. Data science doesn’t always have to be serious. Use install.packages("radlibs") or pip install radlibs to get these packages. Issues and feedback welcome!
Keywords: python, r, packages
stephanie@stephaniekirmer.com
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